Human behavior while decision making is quite complex and uncertain. There are fundamental differences between traditional decision making systems based on sensor data and systems where the agents in the decision making process include humans. The modeling and analysis of human-machine collaborative decision making has become an important research area due to the potential applications in a variety of complex autonomous systems. Incorporating human inputs with physical sensors can be advantageous in enhancing situational assessment for certain situations, and at the same time, brings in technical challenges such as how to characterize the human decision making behavior. In this paper, we discuss some aspects of human-machine networks by focusing on three schemes that include collaborative human decision making with random local thresholds, decision fusion in integrated human-machine networks and binary decision making under cognitive biases. In each case, we aim to optimize the system performance based on appropriate modeling of the human behavior. We also provide a summary of current challenges and research directions related to this problem domain.